Differential privacy (DP) [1]} has received much attention as a solution to the privacy-preserving challenges in machine learning [2]}. By including random noise, such as Gaussian or Laplacian noise distribution, DP offers a significant standard for data privacy protection. The participants manage the degree of privacy budget (\(\epsilon \) ) that defines the number of noises added. Below is a description of the formal definition of DP [1]}.
